metrics-dashboard

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Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.

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npx skill4agent add phuryn/pm-skills metrics-dashboard

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product-metricsdashboard-designkpi-trackingproduct-analyticsalert-thresholds

SKILL.md Content

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Product Metrics Dashboard

Design a comprehensive product metrics dashboard with the right metrics, visualizations, and alert thresholds.

Context

You are designing a metrics dashboard for $ARGUMENTS.
If the user provides files (existing dashboards, analytics data, OKRs, or strategy docs), read them first.

Domain Context

Metrics vs KPIs vs NSM: Metrics = all measurable things. KPIs = a few key quantitative metrics tracked over a longer period. North Star Metric = a single customer-centric KPI that is a leading indicator of business success.
4 criteria for a good metric (Ben Yoskovitz, Lean Analytics): (1) Understandable — creates a common language. (2) Comparative — over time, not a snapshot. (3) Ratio or Rate — more revealing than whole numbers. (4) Behavior-changing — the Golden Rule: "If a metric won't change how you behave, it's a bad metric."
8 metric types: Vanity vs Actionable (only actionable metrics change behavior), Qualitative vs Quantitative (WHAT vs WHY — you need both; never stop talking to customers), Exploratory vs Reporting (explore data to uncover unexpected insights), Lagging vs Leading (leading indicators enable faster learning cycles, e.g. customer complaints predict churn).
5 action steps: (1) Audit metrics against the 4 good-metric criteria. (2) Update dashboards — ensure all key metrics are good ones. (3) Identify vanity metrics — be careful how you use them. (4) Classify leading vs lagging indicators. (5) Pick one problem and dig deep into the data.
For case studies and more detail: Are You Tracking the Right Metrics? by Ben Yoskovitz

Instructions

  1. Identify the metrics framework — organize metrics into layers:
    North Star Metric: The single metric that best captures core value delivery
    Input Metrics (3-5): The levers that drive the North Star
    Health Metrics: Guardrails that ensure overall product health
    Business Metrics: Revenue, cost, and unit economics
  2. For each metric, define:
    MetricDefinitionData SourceVisualizationTargetAlert Threshold
    [Name][Exact calculation: numerator/denominator, time window][Where the data comes from][Line chart / Bar / Number / Funnel][Goal value][When to trigger an alert]
  3. Design the dashboard layout:
    ┌─────────────────────────────────────────────┐
│  NORTH STAR: [Metric] — [Current Value]     │
│  Trend: [↑/↓ X% vs last period]             │
├──────────────────┬──────────────────────────┤
│  Input Metric 1  │  Input Metric 2          │
│  [Sparkline]     │  [Sparkline]             │
├──────────────────┼──────────────────────────┤
│  Input Metric 3  │  Input Metric 4          │
│  [Sparkline]     │  [Sparkline]             │
├──────────────────┴──────────────────────────┤
│  HEALTH: [Latency] [Error Rate] [NPS]       │
├─────────────────────────────────────────────┤
│  BUSINESS: [MRR] [CAC] [LTV] [Churn]        │
└─────────────────────────────────────────────┘
  4. Set review cadence:
    • Daily: Operational health (errors, latency, critical flows)
    • Weekly: Input metrics and engagement trends
    • Monthly: North Star, business metrics, OKR progress
    • Quarterly: Strategic review and metric recalibration
  5. Define alerts:
    • What thresholds trigger investigation?
    • Who gets alerted and through what channel?
    • What's the expected response time?
  6. Recommend tools based on the user's context:
    • Amplitude, Mixpanel, PostHog for product analytics
    • Looker, Metabase, Mode for SQL-based dashboards
    • Datadog, Grafana for operational health
Think step by step. Save the dashboard specification as a markdown document.

Further Reading